Article Archives - Predyktable

Navigating Hospitality’s Choppy Waters: How Predictive Demand Forecasting Can Steer You Towards Success

Posted on March 1, 2024August 19, 2024 by Phillip Sewell

As the CEO of a predictive analytics company, I have seen first-hand the immense pressure facing the hospitality sector. Staff shortages, rising costs, and the ever-growing need for sustainability are creating a perfect storm that threatens to capsize even the most established businesses.

In this blog we explore how predictive demand forecasting can help businesses effectively navigate these challenges.

navigate predictive demand forecasting with Predyktable

The big “4” challenges and the solutions

1. Staff Shortages and Retention:

A staggering 92% of UK hospitality businesses reported staff shortages in Q4 2023, with the situation expected to worsen. (Source: [https://wsta.co.uk/facts-figures/])

Solution: Predictive analytics can help forecast staffing needs based on anticipated occupancy. This allows businesses to schedule more efficiently, avoiding overstaffing during quiet periods and understaffing during peak times. This not only reduces labor costs but also improves employee morale and the overall guest experience.

2. Cost of Living Crisis:

Inflation in the UK hit a 40-year high of 10.5% in December 2023, putting immense pressure on already tight profit margins. (Source: [https://www.ons.gov.uk/economy/inflationandpriceindices/bulletins/consumerpriceinflation/previousReleases])

Solution: Predictive analytics can help businesses optimise their pricing strategies by taking real-time demand and competitor pricing into account. This allows them to maximise revenue without deterring customers by setting prices that reflect market conditions.

3. Increased Running and Ingredient Costs:

From rising energy bills to higher food prices, businesses are facing significant increases in running and ingredient costs. (Source: [https://www.businessgrowthhub.com/blogs/2024/01/challenges-and-opportunities-for-the-hospitality-sector-in-2024])

Solution: Predictive analytics and predictive demand forecasting can help businesses optimise inventory management by forecasting demand for specific items. This helps in reducing waste and allows businesses to purchase only what they need, leading to lower overall costs.

4. Environmental Considerations:

Consumers are increasingly demanding sustainable practices from businesses, with 78% of UK consumers willing to pay more for sustainable travel and accommodation options. (Source: [https://www.ukhospitality.org.uk/2146-2/])

Solution: Predictive analytics can help businesses optimise energy consumption by forecasting occupancy and room usage. This allows them to implement energy-saving measures more effectively, such as adjusting heating and cooling based on real-time needs. This not only benefits the environment but also helps to reduce energy costs.

predictive analytics and predictive demand forecasting with predyktable

Conclusion

In conclusion, the UK hospitality sector is facing a complex landscape in 2024. However, by embracing predictive demand forecasting, businesses can gain valuable insights and make data-driven decisions to navigate these challenges and emerge stronger.

By optimising staffing, pricing, inventory management, and energy consumption, businesses can not only lower operating costs and improve efficiency but also enhance their sustainability and attract environmentally conscious consumers.

Predictive analytics is a powerful tool that can help the hospitality sector weather the storm and chart a course towards success in today’s dynamic market.

Investing in the Future:

Implementing a predictive analytics solution (predictive demand forecasting) might seem like an additional expense at a time when cost reduction is crucial. However, consider it an investment in the future of your business. This technology can be the difference between struggling to survive and thriving in a competitive and ever-changing landscape.

At Predyktable we understand the unique challenges faced by the hospitality industry, and we are committed to providing solutions that are affordable, easy to implement, and impactful. We offer a variety of flexible options to cater to different business needs and budgets.

Demand Forecasting: why external data get’s you closer to the truth

Posted on November 12, 2023August 19, 2024 by Phillip Sewell

In today’s lively and interconnected business landscape, the traditional approach to demand forecasting, relying solely on internal data sources like sales history and inventory levels, may fall short of delivering the precision and adaptability needed to stay competitive. To address this, forward-thinking businesses are recognising the immense value of incorporating external data sources into their demand forecasting strategies. These external data sources open up a wealth of insights, empowering companies to make more informed decisions in the realms of inventory management, staff planning, and marketing campaigns.

The advantages of leveraging external data sources in demand forecasting are manifold, but let’s delve deeper into the rationale and benefits of this strategic shift.

Comprehensive Understanding of Demand Drivers:

External data broadens the horizon of knowledge, providing a holistic view of the forces shaping demand. This comprehensive understanding of demand drivers not only empowers businesses to make more informed decisions, but also positions them to navigate the complexities of today’s interconnected and rapidly changing world with greater agility and precision. Some key areas to capture data are;

Economic Indicators: External data sources, such as GDP growth, unemployment rates, and consumer confidence, provide a macro-economic perspective. These indicators can signal shifts in consumer spending patterns and provide a nuanced view of market dynamics. For example, an uptick in GDP growth might indicate increased consumer confidence, signalling potential growth in demand for certain products or services.

Industry Trends: Keeping an eye on industry trends, competitor activities, and regulatory changes is crucial for staying ahead of the curve. External data sources offer valuable insights into market conditions, new product launches, and evolving customer preferences. This knowledge helps businesses anticipate demand fluctuations and seize opportunities that internal data alone often overlooks.

Social Trends: The digital age is providing us with a goldmine of information. External data sources encompassing product reviews, brand mentions, and customer sentiment can be harnessed to monitor consumer sentiment and track emerging trends. This real-time data enables businesses to respond swiftly to shifts in customer preferences.

Environmental Factors: Weather patterns and other environmental variables can significantly impact demand for certain products. For instance, data on temperature and precipitation can help retailers predict demand for seasonal clothing and adjust inventory accordingly.
Holidays and Special Events: External data sources often include holiday calendars and event schedules (e.g. gigs and sports), which can be crucial for demand forecasting.

External Event impact on Demand Forecasting

By considering these external factors, businesses can elevate their strategic planning across various critical facets of operations, notably in the domains of marketing, promotions, and inventory management.

1- Marketing Campaigns:

Precision Targeting: External data sources, such as holiday calendars and special event schedules, offer a roadmap for businesses to plan their marketing campaigns with precision. By aligning promotions with holidays and special occasions, companies can tap into the heightened consumer interest and capitalise on the increased spending that typically accompanies these events.
Real-time Adaptation: Leveraging external data, allows businesses to adapt their marketing campaigns in real time. Monitoring things such as customer sentiment, national mood, and emerging trends, enables rapid adjustments to messaging and content, ensuring campaigns remain relevant and engaging.
Timing and Messaging: Weather patterns and environmental data can influence not only the timing but also the messaging of marketing campaigns. For instance, if a sudden cold spell is expected, a clothing retailer can craft campaigns around the concept of “stay warm” to boost sales of winter apparel.

2- Promotions:

Optimised Promotional Timings: External data sources, including economic indicators, help in pinpointing the optimal times for promotions. During periods of economic prosperity, customers may be more receptive to premium products, making it an opportune time for high-value promotions. Conversely, during economic downturns, value-driven promotions might resonate better with cost-conscious consumers.
Competitor Monitoring: Industry trends and competitor activities can be invaluable for devising competitive promotions. By keeping an eye on what competitors are doing, businesses can craft promotions that offer a compelling edge in the market.

3- Inventory Management Strategies:

Agile Stocking: Weather data is an asset for businesses with seasonal products. It allows them to anticipate fluctuations in demand and stock inventory accordingly. For instance, a ski equipment retailer can prepare for higher demand during the winter season and reduce inventory during the summer months.
Demand-Driven Inventory: By incorporating external data sources into their demand forecasting models, businesses can synchronise inventory levels with projected demand. This not only reduces the risk of overstocking or stock-outs but also optimises working capital by ensuring that capital is not tied up unnecessarily in excess inventory.

In essence, by thoughtfully integrating external data factors into their planning processes, businesses can be more strategic and proactive in their approach to marketing, promotions, and inventory management. This, in turn, enhances their ability to respond to shifts in consumer behavior and market conditions, ultimately leading to improved operational efficiency, cost reduction, and greater profitability. It is a testament to the growing need for businesses to embrace the holistic insights offered by external data sources in navigating the complexities of today’s marketplace.

External data sources in demand forecasting

Benefits of Enhancing Demand Forecasting with External Data:

Improved Accuracy: External data sources supplement internal data by providing a broader context for demand forecasting. For example, by integrating economic indicators, businesses can better anticipate changes in consumer behavior. Similarly, monitoring social trends can reveal emerging patterns that might be invisible within internal data. The result is more precise forecasts, reducing the margin of error.

Reduced Risk: Businesses often grapple with the challenge of stockout or overstocking. External data sources act as an early warning system, signalling changes in demand patterns. For instance, by incorporating weather data, companies can predict demand variations for seasonal products, thereby mitigating the risk of stockout or excessive inventory holding. Economic indicators can help foresee shifts in discretionary product demand, allowing for more agile inventory management.

Better Decision-Making: The integration of external data sources provides a foundation for sound decision-making across various facets of business operations. For instance, demand forecasts can optimise inventory levels, ensuring products are available where and when customers need them. Additionally, these forecasts can guide production planning, aligning business capacity with anticipated demand.

In Conclusion:

The strategic integration of external data sources into demand forecasting represents a transformative imperative for businesses navigating the dynamic landscapes of today’s markets. This paradigm shift offers a comprehensive range of benefits, including enhanced forecast accuracy, operational risk reduction, data-informed decision-making, improved operational efficiency, cost reduction and increased profitability. The ability to swiftly adapt to market changes further solidifies the case for harnessing external data sources, ensuring that businesses not only survive but thrive in an environment where agility, precision, and data-driven insights are the hallmarks of success. It is a testament to the evolving role of data in shaping the future of business, driving resilience, competitiveness, and sustainable growth.

Leveraging Large Language Models for Enhanced Contextual Understanding at Predyktable

Posted on September 18, 2023July 29, 2024 by Colin Crawford

1- Introduction:

In an ever-evolving world, Predyktable acknowledges the dynamic nature of our surroundings and its profound influence on consumer-business interactions. To navigate these changes effectively, we gather data from diverse sources, encompassing both structured data (e.g. weather and financial indices) and unstructured data (e.g. text and images) and input them into our data pipeline.

Structured data offers a straightforward modelling process, characterised by organisation and logic. For instance, it’s simple to assert that 20 degrees is warmer than 18 degrees. In contrast, unstructured data poses a challenge due to its semantic richness. Defining whether red is superior to green or quantifying the distinctions between Rock and Pop music in a numeric fashion can be intricate tasks.

2- The Role of Large Language Models (LLMs):

Large Language Models (LLMs) represent a category of artificial intelligence systems endowed with the ability to comprehend and generate human language. These models are meticulously trained on vast datasets comprising text and code, enabling them to grasp the subtleties of human language.

Although LLMs’ primary function is to generate information, in the form of chat or code generation, to do so it facilitates the conversion of contextual data into a numeric format that seamlessly integrates into predictive pipelines. For instance, using an LLM, we can encapsulate the disparities between a Taylor Swift concert and a Metallica concert. The LLM, with its linguistic prowess, has learnt that these events attract distinct audiences and can translate this understanding into numeric representations for more robust modelling.

3- Understanding Large Language Models’ Functionality:

LLMs operate by converting textual information into numerical values, subsequently subjecting these values to algorithmic computations—a process commonly referred to as tokenisation. Once tokenised, the LLM leverages its language proficiency to derive the meaning from the text.

For instance, when presented with the sentence “Taylor Swift is a pop singer,” the LLM dissects it, recognising Taylor Swift as a person, a singer, and an artist in the pop genre. It also comprehends the intricate relationships among these concepts. But in reality we don’t need to tell it who Taylor Swift is or how related she is to Kanye, it has already learned this information and can use this to tell us.

4- Advantages of Harnessing Large Language Models for Contextual Data Encoding:

Several advantages emerge from using LLMs to encode contextual data including:

Complex Relationship Capture: LLMs adeptly capture intricate relationships between diverse concepts.
Handling Unquantifiable Data: LLMs empower the representation of challenging-to-quantify data, like distinctions between different event types.

5- A real-world example:

To illustrate how Predyktable employs LLMs for contextual data encoding, consider this scenario:

Imagine Predyktable is partnering with a high-end women’s clothing retailer located in bustling urban areas. The retailer specialises in a wide range of women’s fashion, catering to diverse tastes and preferences. Their objective is to gain a comprehensive understanding of how various events occurring in their target market, influence their sales trends. To achieve this, Predyktable harnesses the power of LLMs proficient in language understanding. Here’s how the process unfolds:

Event Data Encoding: Predyktable starts by collecting data on upcoming events relevant to the retailer’s market. These events could encompass a wide spectrum, including fashion shows, cultural festivals, music concerts, and sporting events. For each event, the LLM is tasked with encoding critical information, such as:

• Event Type: This entails categorising the event, whether it’s a fashion show, music concert, sports game, or any other type.

• Event Date: Precise date information is recorded to establish the timing of the event.

• Event Location: The LLM captures details about where the event is taking place, whether it’s in the retailer’s city or another location.

Clothing Line Data Encoding: Simultaneously, the LLM encodes information about the retailer’s clothing lines. This encompasses a thorough analysis of their diverse product offerings, focusing on factors such as:

• Clothing Type: The LLM differentiates between various clothing categories, such as dresses, tops, pants, and accessories.

• Brand Information: It identifies the brands carried by the retailer, distinguishing between different labels and their respective popularity or prestige.

Building the Predictive Model: With the event and clothing line data successfully encoded by the LLM, Predyktable’s data scientists can proceed to build a predictive model. This model is designed to forecast how diverse events will impact the retailer’s sales. Here’s how this works:

• Event-Product Interaction Analysis: By leveraging the encoded data, the predictive model can analyse how specific types of events affect the sales of particular clothing items. For instance, it can identify whether fashion shows boost the sales of high-end designer dresses or if music concerts have a more significant impact on casual apparel.

• Time Sensitivity: The model considers the timing of events, ensuring that sales predictions consider both the event’s date and the lead-up time.

• Data Integration: It integrates the event data with other relevant factors, such as historical sales data, customer demographics, and marketing efforts, to generate comprehensive forecasts.

Ultimately, this predictive model equips the clothing retailer with invaluable insights. It enables them to make informed decisions about inventory management, marketing strategies, and event participation.

6- Conclusion:

Along with text generation and chat, Large Language Models serve as a potent instrument for numerically encoding contextual data, enriching predictive pipelines. Through the utilisation of LLMs, Predyktable elevates its capacity to construct enhanced models that better serve its clientele.

6.1- Further Considerations:

While LLMs continue to evolve, they have the potential to redefine our interactions with computers. Applications like chatbots, capable of comprehending and responding to natural language and precise machine translation systems bridging language gaps, are on the horizon.

Moreover, LLMs wield a substantial influence on the field of artificial intelligence, contributing to the development of innovative AI models like autonomous vehicles and medical diagnostic systems.

The ongoing evolution of LLMs holds promise for diverse and positive impacts across numerous domains, igniting anticipation for the transformative potential they bear on the world.

Why Now? The Perfect Time for Predictive Analytics in Marketing

Posted on August 22, 2023July 29, 2024 by Phillip Sewell

Introduction

The realm of marketing is in a perpetual state of flux. Emerging technologies, soaring customer expectations, and cutthroat competition have catalysed a landscape that demands nothing short of data-driven prowess. In this dynamic backdrop, the spotlight falls on predictive analytics – an instrumental facet of data science that is set to revolutionise marketing strategies. Predictive analytics leverages historical data to forecast future outcomes, enabling businesses to anticipate demand for goods and services, preempt shifts in customer behaviour, identify trends, and fine-tune marketing campaigns for optimal impact.

The question that beckons is: Why is now the perfect time for predictive analytics to flourish within the realm of marketing? This is the question that we will be exploring within this blog.

Predictive Analytics in Marketing with Predyktable

5 Critical Reasons to Adopt Predictive Analytics Now

While there exists a multitude of reasons to embrace predictive analytics, the five highlighted in this discussion stand out as the most relevant and beneficial in the current landscape. These considerations not only address pressing challenges but also offer actionable solutions that resonate with the contemporary needs of businesses.

The Abundance of Data: The Digital Era has bequeathed an unprecedented treasure trove of data, courtesy of the internet and the ubiquity of mobile devices. This data treasure can be harnessed to train predictive models that unveil accurate predictions about forthcoming behaviours and trends.
The Craving for Real-Time Insights: The rapid tempo of modern business demands nimble decisions to maintain a competitive edge. Predictive analytics can deliver real-time insights into customer behaviour, empowering businesses to make swift, well-informed choices.
The UK skill shortage: The UK’s advertising and marketing industry confronts a significant talent shortage, particularly in data and digital skills. This shortage poses a concern, especially as the UK is the world’s second-largest exporter of advertising services. Predictive analytics can help to address this talent shortage by making it possible for businesses to use data more effectively, analysing data and identify trends and patterns that would be difficult and extremely time consuming to identify manually.
The Soaring Costs of Digital Advertising: The escalating expenses associated with digital advertising necessitate targeted spending for optimal returns. Predictive analytics aids in precise targeting, enhancing the efficacy of marketing campaigns and yielding superior ROI.
Shifts in the Marketing Tech landscape: This complex intersection of data privacy, technological shifts, and ethical concerns poses a multifaceted challenge for businesses. Two areas in particular stand out as an immediate cause for change:

a. Mitigating Third-Party Cookie Impact: Third-party cookies, instrumental in tracking user behaviour for advertising purposes, face growing scrutiny due to privacy concerns. Browser phasing out of these cookies poses a significant challenge for businesses reliant on them. Predictive analytics offers a remedy, utilising historical data to predict user behaviour patterns, thereby circumventing the dependency on third-party cookies. This enables businesses to create accurate user profiles and preferences for more effective targeting and personalisation strategies.

b. Adapting to Evolving Social Media Landscape: Recent policy changes on social media platforms, the advancement of platform technologies, and customers who are more savvy with how businesses use their data, are impeding businesses’ data collection and utilisation efforts. Predictive analytics presents an adaptive approach by analysing historical data to identify customer behaviour patterns beyond social, using national mood and consumer opinion from an array of social, economic and industry sources. This insight forms the foundation for targeted marketing campaigns, which can circumvent the changing limitations of social media platforms.

3 Concrete Applications of Predictive Analytics in Marketing

Predictive analytics wields remarkable potential to elevate marketing strategies, and the moment to capitalise on this transformative power is upon us. While the applications of predictive analytics span a diverse range of business domains, it is within marketing where we see an increasing momentum in the application of advanced analytics. Here are three applications where forward-looking marketing departments are adopting predictive analytics.

Personalising the Customer Experience: By analysing vast quantities of historical data, as well as understanding how current consumer behaviour impacts demand, predictive analytics personalises experiences by suggesting products and services tailored to individual preferences.
Optimising Marketing spend: Optimises ad placement to ensure maximum impact and efficient use of ad spend. Identifies the platforms and channels where high-value audience segments are most active and allocates spend accordingly. By focusing on these channels, predictive analytics maximises reach and engagement while minimising unnecessary ad spend on less effective channels.
Predicting Customer Churn: Anticipating customers at risk of churning allows businesses to take proactive measures to retain them, safeguarding their client base.

These applications are merely the tip of the iceberg in terms of the transformative power of predictive analytics in marketing. To improve marketing outcomes, this tool is indispensable. Beyond these tangible advantages, predictive analytics brings a number of operational benefits to your business. These include:

Risk mitigation: Predictive analytics diminishes uncertainty, guiding businesses toward informed data-driven decisions.
Enhanced Efficiency: Automation and pattern identification, streamlines processes, making businesses more efficient.
Secure a Competitive Edge: Employing predictive analytics to make informed decisions empowers businesses with an edge over their rivals.

To Summarise

In the ever-evolving domain of marketing, staying ahead requires proactive innovation. Predictive analytics, with its ability to enrich decision-making, elevate marketing outcomes, and confer competitive advantage, is an indispensable tool. The time is ripe – embrace predictive analytics to navigate the complex currents of modern marketing with poise and precision.

Revolutionising Marketing Spend Allocation: The Power of Predictive Analytics

Posted on July 5, 2023July 29, 2024 by Andrew Kohter

Introduction

In today’s dynamic and data-driven business landscape, traditional approaches to marketing spend allocation are proving to be inadequate. Manual decision-making processes, once the norm, are plagued by limitations that hinder the effectiveness and efficiency of marketing
campaigns. However, there is a powerful solution on the horizon: predictive analytics. By harnessing the potential of predictive analytics, businesses can overcome the problems associated with traditional marketing spend allocation and unlock new opportunities for growth and success.

In this blog we will examine some of the key issues Marketing teams face today with using traditional methods, and the way that predictive analytics can remove the reliance on them.

Predictive Analytics vs Traditional Methods

Problem 1: Subjectivity and Biases in Decision-Making

One of the key issues with traditional approaches to marketing spend allocation is the heavy reliance on human judgment and intuition. Marketing professionals often make decisions based on personal experiences or assumptions, leading to subjective and biased choices. This can result in suboptimal resource allocation, wasted marketing budgets, and missed opportunities to reach the right audience. However, predictive analytics offers an objective and data-driven alternative.

Solution: Data-Driven Decision-Making
Predictive analytics empowers marketers to make decisions based on concrete data and insights rather than subjective opinions. By leveraging advanced algorithms and machine learning techniques, businesses can analyse vast amounts of customer and market data to
identify valuable patterns, trends, and insights. This data-driven approach ensures that marketing spend is allocated strategically, targeting the right audience with the right message at the right time.

Problem 2: Inability to Respond Quickly to Market Dynamics

Traditional decision-making processes often lack scalability and agility, making it challenging for businesses to respond quickly to changing market conditions such as recessions or economic growth, or customer preferences like sustainability, or cultural trends. By the time decisions are made, the window of opportunity for effective marketing campaigns may have passed, reducing their impact and relevance.

Solution: Real-Time Insights and Rapid Adaptation
Predictive analytics enables businesses to gather and analyse data in real-time, providing up-to-date insights into market dynamics and customer behaviour. By continuously monitoring and analysing data, businesses can swiftly adapt their marketing strategies, allocating spend
where it will yield the best results. This agility ensures that marketing efforts remain relevant, impactful, and aligned with ever-changing market trends.

Problem 3: Inefficient Resource Utilisation

Manual decision-making processes often struggle to effectively analyse and process vast amounts of data. Retailers generate an immense volume of customer and market data, including purchase history, demographic information, online behaviour, and social media interactions. Manually analysing this data becomes impractical and time-consuming, limiting the ability to identify valuable opportunities for targeted marketing and customer segmentation.

Solution: Algorithm Generated Customer Insights
Predictive analytics discovers valuable and useful patterns, trends, and insights from large amounts of data. It’s like searching for hidden treasures within a vast collection of information. By identifying patterns and correlations within customer behaviour, businesses can gain a deep
understanding of their target audience. This enables more precise segmentation, personalised campaigns, optimised ad spend and tailored marketing strategies, ultimately leading to higher conversion rates and customer satisfaction.

Problem 4: Lack of External Data Sources

Traditional approaches to marketing spend allocation often rely solely on internal data sources, limiting the breadth and depth of insights available. Without access to external data, businesses miss out on valuable information that can provide a more comprehensive understanding of the market landscape, competitors, and emerging trends. This lack of external data sources restricts the ability to make informed decisions and allocate marketing spend effectively.

Solution: Integration of External Data and Third-Party Sources
Predictive analytics enables businesses to integrate external data and leverage third-party sources to augment their decision-making process. By incorporating data from social media platforms, industry reports, customer reviews, and other relevant sources, businesses can gain a holistic view of the market. This enriched data ecosystem empowers marketers to make more informed decisions, identify untapped opportunities, and allocate marketing spend based on a broader and more accurate understanding of the market dynamics.

Problem 5: Inaccurate Predictions and Forecasting

Accurate forecasting and prediction are essential for effective marketing spend allocation. However, traditional approaches relying solely on manual processes often fall short in this regard. Predicting customer behaviour and market trends is challenging without leveraging historical data, relevant external data sources, and advanced statistical models.

Solution: Advanced Statistical Modelling and Predictive Algorithms
Predictive analytics empowers businesses to forecast and predict customer behaviour and market trends with a higher degree of accuracy. By analysing historical data, predictive models can identify patterns, detect emerging trends, and make reliable predictions. These insights enable businesses to allocate marketing spend proactively, optimise inventory management, and seize revenue opportunities.

Conclusion:

Traditional approaches to marketing spend allocation are riddled with limitations that can hinder business growth and success. However, by embracing predictive analytics, Organisations can revolutionise their decision-making processes. The power of data-driven insights, real-time adaptation, efficient resource utilisation, and accurate forecasting can unlock new opportunities, drive targeted marketing efforts, optimise return on investment, and enhance overall marketing effectiveness.

Choosing the right tools to unlock the power of Predictive Analytics in Retail

Posted on June 26, 2023July 29, 2024 by Andrew Kohter

Introduction:

In the rapidly evolving field of predictive analytics in retail, there is a tremendous opportunity to leverage the power of data-driven insights to stay ahead in a highly competitive landscape. However, selecting the right predictive analytics tools and technologies is crucial to maximise the benefits. In this blog post, we will explore the 5 key factors retailers should consider when choosing predictive analytics solutions tailored to their unique needs.

1. Integration with Existing Technology Infrastructure:

One of the first considerations for retailers is selecting solutions that seamlessly integrate with their existing technology stack. This includes point of sale systems, inventory management software, and customer relationship management platforms. By ensuring compatibility and smooth integration, retailers can consolidate and analyse diverse datasets, leading to more accurate and comprehensive demand forecasts.

Furthermore, integrating predictive models with existing technology infrastructure promotes operational efficiency. Rather than introducing disparate systems and duplicating efforts, integration streamlines data management and analysis processes.

2. Integration with external data sources

In addition to leveraging internal data, retailers can enhance their predictive analytics capabilities by integrating external data sources. These sources can provide valuable insights into market trends, customer behaviour, and competitive landscape. When selecting predictive analytics tools, retailers should prioritise solutions that offer compatibility and easy integration with various external data sources, such as social media, weather data, economic data, demographic data, and industry reports. By incorporating this external data, retailers can gain a holistic view of their market and customers, leading to more accurate and robust predictive models.

3. Scalability, Customisability, and Flexibility:

Scalability, customisability, and flexibility are critical factors for predictive analytics tools. Retailers should opt for solutions that can handle large volumes of data and adapt to changing business needs. Scalable tools ensure efficient processing and analysis of data, regardless of an organisation’s growth or fluctuations in demand.

Customisation allows retailers to fine-tune predictive models to align with their specific business requirements, product assortment, and customer segments. Retailers operate in diverse markets with varying customer behaviours and preferences. By customising predictive models, retailers can capture the nuances and intricacies of their customer base, resulting in more accurate forecasts and tailored insights. This flexibility empowers retailers to understand and respond to their customers’ changing demands, ultimately driving customer satisfaction and loyalty.

Furthermore, flexibility in predictive models enables retailers to adapt to the ever-evolving business landscape. Retail organisations experience fluctuations in demand, seasonal variations, market trends, and other external factors that impact their operations. Flexible models can accommodate these changes, allowing retailers to recalibrate their predictions and adjust their strategies accordingly. By being adaptable, predictive models can provide real-time insights and recommendations that reflect the current market conditions, empowering retailers to make agile and informed decisions.

4. Usability and Accessibility:

Usability and accessibility play a vital role in the successful adoption of predictive analytics tools within retail organizations. Retailers should prioritise user-friendly interfaces that enable business users and analysts to interact with data and models easily and intuitively. The availability of visualisation capabilities, interactive dashboards, and self-service analytics empowers stakeholders at different levels to derive insights and make informed decisions. Cloud-based solutions offer convenience, scalability, and real-time access to data, allowing retailers to leverage predictive analytics capabilities from anywhere, anytime.

An often overlooked but critical step in implementing predictive analytics solutions is change management. Retailers must consider how to effectively manage the organisational change that comes with adopting these tools. This involves training and upskilling employees, fostering a data-driven culture, and ensuring buy-in from key stakeholders. By addressing the human element of change, retailers can maximise the adoption and utilisation of predictive analytics tools, ensuring a smooth transition and long-term success.

5. Robustness and Accuracy:

The robustness and accuracy of predictive analytics tools are paramount. Retailers must evaluate the algorithms, machine learning capabilities, and statistical models employed by the solutions. These tools should demonstrate proficiency in handling various forecasting scenarios, including seasonality, promotional effects, and demand volatility. Continuous model monitoring, validation, and recalibration ensure the accuracy and reliability of forecasts over time, enabling retailers to make data-driven decisions with confidence.

It is essential to regularly reassess the model’s performance, evaluate its alignment with the current business environment, and update it with new data and insights. Incorporating external data sources and maintaining a feedback loop with subject matter experts can help ensure that the model remains relevant and provides meaningful predictions in a changing landscape.

Predyktable Data for predictive analytics in Retail

Conclusion:

By carefully considering these factors and selecting predictive analytics tools and technologies that align with their specific needs, retailers can unlock the full potential of data-driven insights. With the right tools in place, retailers can improve demand forecasting accuracy, optimise inventory management, and ultimately drive business growth. Embracing predictive analytics in retail is no longer a luxury; it is a necessity for staying competitive in today’s rapidly changing marketplace.

Are you ready to embrace the power of predictive analytics in retail and revolutionise your business?

Why smart retailers are checking out prescriptive analytics

Posted on February 22, 2023July 29, 2024 by Phillip Sewell

“Most retailers are overwhelmed with vast data volumes offering little or no recommendations on what it means to them.” says Predyktable co-founder and CEO Phillip Sewell

For many retailers trying to navigate a climate of perpetual change, making correct business-critical calls on complex environmental, economic and consumer future outcomes is an expensive gamble. This is because current business intelligence and data analytics approaches that support retailers’ decision-making, no longer cut it.

Traditionally, business intelligence and data analytics have helped retailers understand and influence their customers’ buying habits. But despite billions of pounds spent globally on data platforms, data repositories and a whole stack of tools, most retail professionals still lack the support they need to turn data into forward actions that maximise profits.

Most retailers are overwhelmed with vast data volumes offering little or no recommendations on what it means to them. Many also use solutions heavily reliant on historic data and insights that aren’t tailored for their specific forward-thinking needs. There’s not enough focus on identifying and understanding wider external data sources. This manual time-consuming research isn’t being done, so the data quality and depth aren’t there to support accurate predictions.

These approaches aren’t enough to help retailers form a clear future view and know what to do about it.

A forward-looking catalyst

To solve these issues, there’s an increasingly sophisticated capability that’s taking data analytics way beyond explanations and predictions. Welcome to ‘prescriptive analytics’, which is widely considered the fourth stage of data analytics’ evolution. Here’s where it sits:

Descriptive analytics – what happened?
Diagnostic analytics – why did it happen?
Predictive analytics – what might happen in the future?
Prescriptive analytics – what should we do next?

Prescriptive analytics aims to look into the future and then recommend the best course of action.

Marks & Spencer and John Lewis are among a growing number of retailers using prescriptive analytics to ‘look into the future’ and pre-empt trading conditions in the weeks, months and years ahead. For example, M&S uses this approach to guide its design, buying and pricing decisions across thousands of product lines in 50 categories, including apparel, lingerie, footwear, accessories, food, home and beauty.

Why prescriptive analytics delivers more as a managed service

When it comes to outsourcing this capability, many retailers miss out by working with conventional data providers offering prescriptive analytics as a bolt-on, one-off piece of work – with minimal support. I believe that achieving valuable results with prescriptive analytics isn’t possible with off the shelf or piecemeal solutions that treat retailers as commodities.

It’s better opting for a partner offering prescriptive analytics as a fully managed service, backed by retail sector experience. They must focus on understanding a retailer’s business and specific challenges – as these are crucial factors underpinning success. Retailers must also be supported every step of the way, so they keep solving new challenges facing their business.

The best prescriptive analytics services blend descriptive, diagnostic and predictive insights, with cutting-edge artificial intelligence, machine learning, automation, genuine data science and in-sector consultancy expertise. Everything should be custom built, with each step creating prescription models precisely choreographed to meet retailers’ individual needs.

This means enhancing internal data, with much wider external insights including global & local trends: weather, travel, localised demand spikes, and more. Using this high-quality data, data scientists build and optimise prescription models which identify previously elusive, connected, patterns to deliver the most accurate foresight fuelled prescriptions.

Expect data scientists to continually find new insights to keep models relevant, while learning from the data so they keep delivering value. By uniquely aggregating data from a wider range of external sector sources, models are further enriched to provide greater accuracy and depth to foresight, so the prescription models keep getting better and retailers keep making the most profitable business decisions.

Use case – customer sentiment

Here’s an example of how retailers can better gauge brand sentiment through the voice of the customer with prescriptive analytics.

Current analytics tools offer limited views on what’s being said about brand, as they mainly focus on social media analysis and sample surveys. They don’t show how retailers are perceived through all online and offline touchpoints. By not involving sentiment in predictions, means less accurate, decision-making.

A better approach is to create machine learning models connected to everywhere that customers are talking about the brand. This means covering online and offline channels, social media platforms, rating & review sites, search engines, contact centre logs, chat bots, blog posts, and more.

Natural language processing is then used to contextualise each interaction. This means establishing if it’s voiced as a positive, neutral, or negative opinion, if this opinion is shared by anyone else, and if so, what’s the commonality between them?

Sentiment and activity hotspots are gauged across customer segments, location, and channels. These insights are enhanced with domain models that track behaviours at a national and regional level. This means determining if brand sentiment is part of a wider opinion shift, or if it’s unique to customers – because of a retailer’s actions.

All this activity generates rich foresight that fuels recommendations on which new products to launch or territories to explore. By also dynamically forecasting demand, enables retailers to optimise the cost of entering new customer segments.

What gains can prescriptive analytics deliver for retail?

There’s huge value and so many positive outcomes to be gained with prescriptive analytics as a service, some of these include:

Know which areas to reduce cost: including marketing spend, labour optimisation and demand forecasting.
Understand exactly where to make more money within the most profitable customer segments.
Identify which customers are most likely to convert, then win them over with a hyper-personalised and engaging shopping experiences.
Retain high-value customers by recommending products and services that complement customers’ existing purchase history, interests and lifestyle.
Better optimise pricing at a regional level to maximise the profit opportunities.

Final thoughts

Whatever your size, Predyktable delivers prescriptive analytics as a fully managed service to generate actionable foresight faster, without complexity and compromise. To discuss how we can help your organisation make more profitable decisions, please drop us a line, we’d love a chat.